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TIMBER: On supporting data pipelines in Mobile Cloud Environments
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Collecting, storing, and providing access to Internet of Things (IoT) data are fundamental tasks to many smart city projects. However, developing and integrating IoT systems is still a significant barrier to entry. In this work, we share insights on the development of cloud data storage and visualization tools for IoT smart city applications using flood warning as an example application. The developed system incorporates scalable, autonomous, and inexpensive features that allow users to monitor real-time environmental conditions, and to create threshold-based alert notifications. Built in Amazon Web Services (AWS), the system leverages serverless technology for sensor data backup, a relational database for data management, and a graphical user interface (GUI) for data visualizations and alerts. A RESTful API allows for easy integration with web-based development environments, such as Jupyter notebooks, for advanced data analysis. The system can ingest data from LoRaWAN sensors deployed using The Things Network (TTN). A cost analysis can support users’ planning and decision-making when deploying the system for different use cases. A proof-of-concept demonstration of the system was built with river and weather sensors deployed in a flood prone suburban watershed in the city of Charlottesville, Virginia.
Major advances in telecommunications and the Internet of Things have given rise to numerous smart city scenarios in which smart services are provided. What was once a dream for the future has now become reality. However, the need to provide these smart services quickly, efficiently, in an interoperable manner and in real time is a cutting-edge technological challenge. Although some software architectures offer solutions in this area, these are often limited in terms of reusability and maintenance by independent modules —involving the need for system downtime when maintaining or evolving, as well as by a lack of standards in terms of the interoperability of their interface. In this paper, we propose a fully reusable microservice architecture, standardized through the use of the Web of Things paradigm, and with high efficiency in real-time data processing, supported by complex event processing techniques. To illustrate the proposal, we present a fully reusable implementation of the microservices necessary for the deployment of the architecture in the field of air quality monitoring and alerting in smart ports. The performance evaluation of this architecture shows excellent results.
Function-as-service (FaaS) platforms promise a simpler programming model for cloud computing, in which the developers concentrate on writing its applications. In contrast, platform providers take care of resource management and administration. As FaaS users are billed based on the execution of the functions, platform providers have a natural incentive not to keep idle resources running at the platform's expense. However, this strategy may lead to the cold start issue, in which the execution of a function is delayed because there is no ready resource to host the execution. Cold starts can take hundreds of milliseconds to seconds and have been a prohibitive and painful disadvantage for some applications. This work describes and evaluates a technique to start functions , which restores snapshots from previously executed function processes. We developed a prototype of this technique based on the CRIU process checkpoint/restore Linux tool. We evaluate this prototype by running experiments that compare its start-up time against the standard Unix process creation/start-up procedure. We analyze the following three functions: i) a "do-nothing" function, ii) an Image Resizer function, and iii) a function that renders Markdown files. The results attained indicate that the technique can improve the start-up time of function replicas by 40% (in the worst case of a "do-nothing" function) and up to 71% for the Image Resizer one. Further analysis indicates that the runtime initialization is a key factor, and we confirmed it by performing a sensitivity analysis based on synthetically generated functions of different code sizes. These experiments demonstrate that it is critical to decide when to create a snapshot of a function. When one creates the snapshots of warm functions, the speed-up achieved by the prebaking technique is even higher: the speed-up increases from 127.45% to 403.96%, for a small, synthetic function; and for a bigger, synthetic function, this ratio increases from 121.07% to 1932.49%.
New architectural patterns (e.g. microservices), the massive adoption of Linux containers (e.g. Docker containers), and improvements in key features of Cloud computing such as auto-scaling, have helped developers to decouple complex and monolithic systems into smaller stateless services. In turn, Cloud providers have introduced serverless computing, where applications can be defined as a workflow of event-triggered functions. However, serverless services, such as AWS Lambda, impose serious restrictions for these applications (e.g. using a predefined set of programming languages or difficulting the installation and deployment of external libraries). This paper addresses such issues by introducing a framework and a methodology to create Serverless Container-aware ARchitectures (SCAR). The SCAR framework can be used to create highly-parallel event-driven serverless applications that run on customized runtime environments defined as Docker images on top of AWS Lambda. This paper describes the architecture of SCAR together with the cache-based optimizations applied to minimize cost, exemplified on a massive image processing use case. The results show that, by means of SCAR, AWS Lambda becomes a convenient platform for High Throughput Computing, specially for highly-parallel bursty workloads of short stateless jobs.
In recent years distributed processing frameworks such as Apache Spark have been utilized for running big data
applications. Predicting the application’s execution time has been an important goal since it can help the end user to determine the necessary processing resources to be reserved. While there have been some previous works that examine the problem of profiling Spark applications, they mainly focus on specific application types (e.g., Machine learning applications) and rely on the existence of a large number of previous execution runs. In this work we aim at overcoming these limitations by minimizing the number of past execution runs needed for the profiling phase. Furthermore, we identify patterns of continuous identical dataset transformations between different applications to cope with the limited historical data availability. We propose an on-line profiling framework, called Dione, that estimates the running times of new applications, even if no historical data is available. Finally, in our detailed experimental evaluation, using practical workloads on our local cluster, we illustrate that our approach accurately predicts the execution times of Spark applications and requires 30% less training time and monetary cost compared to the current state-of-the art techniques.
Graph data have become ubiquitous and manipulating them based on similarity is essential for many applications. Graph edit distance is one of the most widely accepted measures to determine similarities between graphs and has extensive applications in the fields of pattern recognition, computer vi- sion etc. Unfortunately, the problem of graph edit distance computation is NP-Hard in general. Accordingly, in this pa- per we introduce three novel methods to compute the upper and lower bounds for the edit distance between two graphs in polynomial time. Applying these methods, two algorithms AppFull and AppSub are introduced to perform different kinds of graph search on graph databases. Comprehensive experimental studies are conducted on both real and syn- thetic datasets to examine various aspects of the methods for bounding graph edit distance. Result shows that these methods achieve good scalability in terms of both the number of graphs and the size of graphs. The effectiveness of these algorithms also confirms the usefulness of using our bounds in filtering and searching of graphs.
Mobile crowdsensing (MCS) has emerged as a new revolutionary paradigm to collect large-scale data by the crowd. However, there is a lack of a holistic system than can provide an integrated design for this large volumes data coming from different sensors, and afford analytics capabilities. This paper provides an envision of a micro-service based architecture for implementing and automating ETL data management pipelines in MCS.
This study builds a fully deconvolutional neural network (FDNN) and addresses the problem of single image super-resolution (SISR) by using the FDNN. Although SISR using deep neural networks has been a major research focus, the problem of reconstructing a high resolution (HR) image with an FDNN has received little attention. A few recent approaches toward SISR are to embed deconvolution operations into multilayer feedforward neural networks. This paper constructs a deep FDNN for SISR that possesses two remarkable advantages compared to existing SISR approaches. The first improves the network performance without increasing the depth of the network or embedding complex structures. The second replaces all convolution operations with deconvolution operations to implement an effective reconstruction. That is, the proposed FDNN only contains deconvolution layers and learns an end-to-end mapping from low resolution (LR) to HR images. Furthermore, to avoid the oversmoothness of the mean squared error loss, the trained image is treated as a probability distribution, and the Kullback–Leibler divergence is introduced into the final loss function to achieve enhanced recovery. Although the proposed FDNN only has 10 layers, it is successfully evaluated through extensive experiments. Compared with other state-of-the-art methods and deep convolution neural networks with 20 or 30 layers, the proposed FDNN achieves better performance for SISR.
Cloud computing has recently emerged in the landscape of Information Technology as a compelling paradigm for managing and delivering services over the Internet in a performance- and cost-effective way. However, its development is still at its infancy, with many issues worthy to be investigated. In this chapter, we analyze the problem of service level provisioning and the possible strategies that can be used to tackle it at the various layers of the cloud architecture, focusing on the perspective of cloud-based application providers. We also propose an approach for the dynamic QoS provisioning of cloud-based applications which takes into account that the provider has to fulfill the service level settled with the application users while minimizing the resources outsourced from the cloud infrastructure in such a way to maximize its profits.
Function as a Service (FaaS) has been gaining popularity as a way to deploy computations to serverless backends in the cloud. This paradigm shifts the complexity of allocating and provisioning resources to the cloud provider, which has to provide the illusion of always-available resources (i.e., fast function invocations without cold starts) at the lowest possible resource cost. Doing so requires the provider to deeply understand the characteristics of the FaaS workload. Unfortunately, there has been little to no public information on these characteristics. Thus, in this paper, we first characterize the entire production FaaS workload of Azure Functions. We show for example that most functions are invoked very infrequently, but there is an 8-order-of-magnitude range of invocation frequencies. Using observations from our characterization, we then propose a practical resource management policy that significantly reduces the number of function cold starts, while spending fewer resources than state-of-the-practice policies.
Serverless computing has emerged as a new cloud computing execution model that liberates users and application developers from explicitly managing ‘physical’ resources, leaving such a resource management burden to service providers. In this article, we study the problem of resource allocation for multi-tenant serverless computing platforms explicitly taking into account workload fluctuations including sudden surges. In particular, we investigate different root causes of performance degradation in these platforms where tenants (their applications) have different workload characteristics. To this end, we develop a fine-grained CPU cap control solution as a resource manager that dynamically adjusts CPU usage limit (or CPU cap) concerning applications with same/similar performance requirements, i.e.,
application groups
. The adjustment of CPU caps applies primarily to co-located worker processes of serverless computing platforms to minimize resource contention, which is the major source of performance degradation. The actual adjustment decisions are made based on performance metrics (e.g., throttled time and queue length) using a group-aware scheduling algorithm. The extensive experimental results performed in our local cluster confirm that the proposed resource manager can effectively eliminate the burden of explicit reservation of computing capacity, even when fluctuations and sudden surges in the incoming workload exist. We measure the robustness of the proposed resource manager by comparing it with several heuristics which extensively used in practice, including the enhanced version of round robin and the least length queue scheduling policies, under various workload intensities driven by real-world scenarios. Notably, our resource manager outperforms other heuristics by decreasing skewness and average response time up to 44 and 94 percent, respectively, while it does not over-use the CPU resources.
We present ByteScheduler, a generic communication scheduler for distributed DNN training acceleration. ByteScheduler is based on our principled analysis that partitioning and rearranging the tensor transmissions can result in optimal results in theory and good performance in real-world even with scheduling overhead. To make ByteScheduler work generally for various DNN training frameworks, we introduce a unified abstraction and a Dependency Proxy mechanism to enable communication scheduling without breaking the original dependencies in framework engines. We further introduce a Bayesian Optimization approach to auto-tune tensor partition size and other parameters for different training models under various networking conditions. ByteScheduler now supports TensorFlow, PyTorch, and MXNet without modifying their source code, and works well with both Parameter Server (PS) and all-reduce architectures for gradient synchronization, using either TCP or RDMA. Our experiments show that ByteScheduler accelerates training with all experimented system configurations and DNN models, by up to 196% (or 2.96X of original speed).
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif- ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implemen- tation of the convolution operation. To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called dropout that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry
Recurrent Neural Networks are powerful tools for modeling sequences. They are flexibly extensible and can incorporate various kinds of information including temporal order. These properties make them well suited for generating sequential recommendations. In this paper, we extend Recurrent Neural Networks by considering unique characteristics of the Recommender Systems domain. One of these characteristics is the explicit notion of the user recommendations are specifically generated for. We show how individual users can be represented in addition to sequences of consumed items in a new type of Gated Recurrent Unit to effectively produce personalized next item recommendations. Offline experiments on two real-world datasets indicate that our extensions clearly improve objective performance when compared to state-of-the-art recommender algorithms and to a conventional Recurrent Neural Network.
Cloud computing has recently emerged in the landscape of Information Technology as a compelling paradigm for managing and delivering services over the Internet in a performance- and cost-effective way. However, its development is still at its infancy, with many issues worthy to be investigated. In this chapter, we analyze the problem of service level provisioning and the possible strategies that can be used to tackle it at the various layers of the cloud architecture, focusing on the perspective of cloud-based application providers. We also propose an approach for the dynamic QoS provisioning of cloud-based applications which takes into account that the provider has to fulfill the service level settled with the application users while minimizing the resources outsourced from the cloud infrastructure in such a way to maximize its profits.
Graph data have become ubiquitous and manipulating them based on similarity is essential for many applications. Graph edit distance is one of the most widely accepted measures to determine similarities between graphs and has extensive applications in the fields of pattern recognition, computer vision etc. Unfortunately, the problem of graph edit distance computation is NP-Hard in general. Accordingly, in this paper we introduce three novel methods to compute the upper and lower bounds for the edit distance between two graphs in polynomial time. Applying these methods, two algorithms AppFull and AppSub are introduced to perform different kinds of graph search on graph databases. Comprehensive experimental studies are conducted on both real and synthetic datasets to examine various aspects of the methods for bounding graph edit distance. Result shows that these methods achieve good scalability in terms of both the number of graphs and the size of graphs. The effectiveness of these algorithms also confirms the usefulness of using our bounds in filtering and searching of graphs.
This survey is divided into three major sections. The first concerns mathematical results about the choice axiom and the choice models that devoIve from it. For example, its relationship to Thurstonian theory is satisfyingly understood; much is known about how choice and ranking probabilities may relate, although little of this knowledge seems empirically useful; and there are certain interesting statistical facts. The second section describes attempts that have been made to test and apply these models. The testing has been done mostly, though not exclusively, by psychologists; the applications have been mostly in economics and sociology. Although it is clear from many experiments that the conditions under which the choice axiom holds are surely delicate, the need for simple, rational underpinnings in complex theories, as in economics and sociology, leads one to accept assumptions that are at best approximate. And the third section concerns alternative, more general theories which, in spirit, are much like the choice axiom. Perhaps I had best admit at the outset that, as a commentator on this scene, I am quali- fied no better than many others and rather less well than some who have been working in this area recently, which I have not been. My pursuits have led me along other, somewhat related routes. On the one hand, I have contributed to some of the recent, purely algebraic aspects of fundamental measurement (for a survey of some of this material, see Krantz, Lute, Suppes, & Tversky, 1971). And on the other hand, I have worked in the highly probabilistic area of psychophysical theory; but the empirical materials have led me away from axiomatic structures, such as the choice axiom, to more structural, neural models which are not readily axiomatized at the present time. After some attempts to apply choice models to psychophysical phenomena (discussed below in its proper place), I was led to conclude that it is not a very promising approach to, these data, and so I have not been actively studying any aspect of the choice axiom in over 12 years. With that understood, let us begin.
Error-tolerant graph matching is a powerful concept that has various applications in pattern recognition and machine vision. In the present paper, a new distance measure on graphs is proposed. It is based on the maximal common subgraph of two graphs. The new measure is superior to edit distance based measures in that no particular edit operations together with their costs need to be defined. It is formally shown that the new distance measure is a metric. Potential algorithms for the efficient computation of the new measure are discussed.
SOCK: Rapid task provisioning with serverless-optimized containers
- Oakes
Serverless computation with
- S Hendrickson
Generalized cross entropy loss for training deep neural networks with noisy labels
- Zhang
Uses and abuses of the cross-entropy loss: Case studies in modern deep learning
- Gordon-Rodriguez
Similarity of neural network representations revisited
- Kornblith
Static call graph construction in aws lambda serverless applications
- Obetz
Faasm: Lightweight isolation for efficient stateful serverless computing
- Shillaker
Serverless computation with OpenLambda
- Hendrickson
Neural networks trained to solve differential equations learn general representations
- Magill
ModelKeeper: Accelerating DNN training via automated training warmup
- Lai
Edgewise: a better stream processing engine for the edge
- Fu